Are you looking for a step-by-step Deep Learning Roadmap?… If yes, this article is for you. This article will provide a complete Deep Learning Roadmap from scratch. Along with that, you will also find some best resources to learn Deep Learning concepts.
Now without any further ado, let’s get started-
Deep Learning Roadmap 2025
Before I discuss the Deep Learning Roadmap, let’s see the Skills Required for Deep Learning–
Deep Learning is becoming popular day by day. Having knowledge of Deep Learning is also important along with Machine Learning.
So, to learn Deep Learning, you should have the following 6 skills-
- Maths Skills.
- Programming Skills.
- Data Engineering Skills.
- Machine Learning Knowledge.
- Knowledge of DL Algorithms.
- Knowledge of DL Frameworks.
Now, let’s move to the step-by-step Deep Learning Roadmap–
Step 1- Brush-Up Your Math skills
The first step or skill in deep learning is mathematical skills. It helps you to understand how deep learning and machine learning algorithms work. In mathematics, you need to learn the following subjects-
Now, let’s see how all these subjects’ knowledge will help you in machine learning and in deep learning. But before that, let me clear one thing, don’t think you can directly jump into deep learning without learning machine learning. That’s why I am discussing all the skills that are required for deep learning as well as machine learning.
a) Probability & Statistics-
In probability, there is Bayes Theorem. This is used in the Naive Bayes Algorithm to categorize our data. The next one is Probability Distribution. This will help you to determine how frequently an event can take place. You must also learn how Sampling and hypothesis testing works.
b) Linear Algebra-
In Linear Algebra, there are two main concepts that are used in deep learning and machine learning- Matrices and Vectors. They are both used broadly in deep learning. Matrices are used in Image Recognition. The image you use for image recognition is in the form of matrices.
The recommender system you see in Amazon and in Netflix actually works on the vector. This vector is the customer behavior vector.
c) Calculus-
In calculus, you have Differential calculus and Integral calculus. They help to determine the probability of events. For example, in finding the posterior probability in the Naive Bayes Algorithm.
Now, let’s see the resources to learn math and statistics-
-Resources for Learning Statistics & Maths-
- Intro to Statistics (Udacity Free Course)
- Linear Algebra Refresher Course(Udacity Free Course)
- Basic Statistics (Online Course)
- Statistics and probability (Khan Academy)
- Practical Statistics for Data Scientists (TextBook)
- Data Science: Statistics and Machine Learning Specialization (Online Course)
- Statistics for Data Science (YouTube Video)
- Mathematics for Data Science Specialization (Online Course)
- Khan Academy
- Data Science Math Skills (Online Course)
Step 2- Learn Programming Language
You need to develop good programming skills if you wanna become a deep learning expert. There are lots of programming languages are available, you can choose from. The most used programming languages in Deep learning are-
- Python.
- R.
- C.
- Java.
But, Python and R are the most suitable programming language for deep learning and machine learning. I would suggest you learn Python or R.
So, if you are a beginner, I will recommend you, learn Python.
Now, let’s see the resources to learn Python and R.
-Resources for Learning Python Programming-
- Introduction to Python Programming(Udacity Free Course)
- The Python Tutorial (PYTHON.ORG)
- CS DOJO (YouTube)
- Python 3 Tutorial (SOLOLEARN)
- Python For Data Science(Udemy Free Course)
- Programming with Mosh (YouTube)
- Corey Schafer (YouTube)
I am also going to list some free resources to learn R Programming. So, If you want to learn R, you can learn from these Free resources-
-Free Resources to Learn R-
- R Basics – R Programming Language Introduction(Udemy Free Course)
- R Programming (Coursera Free to Audit Course)
- Learn R Quickly (Udemy Free Course)
- R, ggplot, and Simple Linear Regression (Udemy Free Course)
- R Programming Tutorial (YouTube Tutorial)
- R Programming Full Course In 7 Hours (YouTube Tutorial)
Step 3- Learn Data Wrangling
You should have some Data Wrangling Skills. Deep learning works on a huge amount of data. Therefore, you should have knowledge of dealing with this data. Data Wrangling skills include-
a) Data Pre-processing- Data pre-processing requires the following steps-
- Cleaning.
- Parsing.
- Correcting.
- Consolidating.
b) ETL (Extraction, Transformation, Load)-
You should know how to extract the data from the internet or a local server. You need to know how to transform the data. Transformation means converting your data into a proper format that is acceptable. The next one is loading, so you need to know how to load the data into your program.
c) Knowledge of Database-
Deep Learning is all about data, so you should have knowledge of the database. You need to have knowledge of MySql, Oracle Database, and NoSql.
Now, let’s see the resources to learn Data Wrangling and SQL.
-Resources for Learning Data Wrangling and SQL.–
- W3Schools
- SQL for Data Analysis(Udacity Free Course)
- SQL for Data Science (edX Free to Audit Course)
- SQL for Data Analysis: Solving real-world problems with data(Udemy Free Course)
- SQL Crash Course for Aspiring Data Scientist(Udemy Free Course)
- SQL Tutorial
Step 4- Learn Machine Learning Concepts
The next most important skill is to learn machine learning algorithms. Because in order to learn deep learning, you should have basic knowledge of machine learning algorithms. At least learn some popular machine learning algorithms. For eg-
- Naive Bayes.
- Support Vector Machine.
- K nearest Neighbour.
- Linear Regression.
- Logistic Regression.
- Decision Tree.
- Random Forest.
- K means Clustering.
- Hierarchical Clustering.
- Apriori.
Some of these algorithms fall into Classification Category, some in Clustering Category.
In classification, there are two categories– classification, and regression. Classification algorithms classify the data into different categories, whereas, a regression predicts the value of data.
In clustering, data is partitioned into a different cluster based on certain similar attributes.
Now, let’s see the resources to learn Machine Learning-
-Resources for Learning Machine Learning–
- Machine Learning by Georgia Tech(Udacity Free Course)
- Introduction to Machine Learning Course(Udacity Free Course)
- Machine Learning: Unsupervised Learning (Udacity Free Course)
- Machine Learning by Stanford University(Coursera Free to Audit Course)
- Machine Learning for All by University of London(Coursera Free to Audit Course)
- What is Machine Learning?(Udemy Free Course)
- Machine Learning Fundamentals(edX Free to Audit Course)
Step 5- Learn Deep Learning Algorithms
After Machine Learning Algorithm, you need to learn a deep learning algorithm. The most common and popular Deep Learning algorithms are-
- Artificial Neural Network.
- Convolutional Neural Network.
- Recurrent Neural Network.
- Generative Adversarial Network.
- Deep Belief Network.
- Long Short Term Memory Network.
Once, you learned these algorithms, you should learn how to-
- Select a Problem.
- Choose an appropriate algorithm for your problem.
- Create a model with one or more algorithms.
- Optimize your model for the best accuracy.
Now, let’s see the resources to learn Deep Learning-
-Resources for Learning Deep Learning–
- Deep Learning Specialization (deeplearning.ai)
- Deep Learning– Udacity
- Intro to Deep Learning with PyTorch– Udacity FREE Course
- Intro to TensorFlow for Deep Learning– Udacity FREE Course
- Intro to Deep Learning– Kaggle
- Generative Adversarial Networks (GANs) Specialization– Coursera
- Become a Deep Reinforcement Learning Expert– Udacity
- Deep Learning: Convolutional Neural Networks in Python– Udemy
- Reinforcement Learning– Udacity
- Neural Networks and Deep Learning– Coursera
Step 6- Learn Deep Learning Frameworks
You should have knowledge of Deep Learning Frameworks.
The most popular framework of Deep Learning-
- TensorFlow.
- Theano.
- scikit learn.
- PyTorch.
- Keras.
- DL4J.
- Caffe.
- Microsoft Cognitive Toolkit.
Now, let’s discuss some framework in detail-
a) Tensorflow-
Tensorflow is the most widely used framework in Machine Learning and Deep Learning. It is an open-source software library. It is used for numerical computation using the data flow graph.
b) Theano-
Theano helps you define, optimize, and evaluate mathematical operations. LASAGNE, BLOCKS, and KERAS are popular libraries.
c) scikit learn-
It is built on top of existing libraries like NUMPY, SCIPY, and MATPLOTLIB. It has started as a GOOGLE SUMMER OF CODE and now has 23,000 Github commits.
Now, let’s see the resources to learn Deep Learning Frameworks–
-Resources for Learning Deep Learning Frameworks–
- Intro to Deep Learning with PyTorch– Udacity FREE Course
- Intro to TensorFlow for Deep Learning– Udacity FREE Course
- Introduction to Deep Learning & Neural Networks with Keras– Coursera
- Advanced Deep Learning with Keras– Datacamp
- Deep Learning Fundamentals with Keras– edX
- Complete Tensorflow 2 and Keras Deep Learning Bootcamp- Udemy
- TensorFlow 2 for Deep Learning Specialization– Coursera
- Introduction to Deep Learning with PyTorch– DataCamp
- Deep Neural Networks with PyTorch– Coursera
- PyTorch: Deep Learning and Artificial Intelligence– Udemy
- PyTorch for Deep Learning with Python– Udemy
- PyTorch Tutorials– pytorch.org
Step 7- Work on Deep Learning Projects
Once you learn all the required deep learning skills, start working on deep learning projects. The more your work on projects, the more you will learn.
I am going to discuss 8 Deep Learning Project Ideas for Beginners. These projects will help you to sharpen your deep learning skills and boost your resume. I would suggest you pick a project from this list and start working on that project.
-Deep Learning Projects-
- Dog’s Breed Identification
- Face Detection
- Crop Disease Detection
- Image Classification with CIFAR-10 Dataset
- Handwritten Digit Recognition
- Color Detection
- Real-Time Image Animation
- Driver Drowsiness Detection
You can also check this article for these Deep Learning Projects- 8 Deep Learning Project Ideas for Beginners
So that’s all, only these skills are required to become a Deep Learning Expert. Congratulations, it’s your first step towards deep learning.
But the most important thing is to keep enhancing your skills by working on more and more challenges.
The more you practice, the more knowledge of deep learning you will gain. So after completing these steps, don’t stop, just find new challenges and try to solve them.
Now it’s time to wrap up!
Conclusion
In this article, I have discussed a step-by-step Deep Learning Roadmap 2025. If you have any doubts or queries, feel free to ask me in the comment section. I am here to help you.
All the Best for your Career!
Happy Learning!
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Thank YOU!
Learn Deep Learning Basics here.
Though of the Day…
‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.
– Henry Ford
Written By Aqsa Zafar
Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.